REPAC

REPAC analyzes alternative polyadenylation (APA) from RNA-sequencing (RNA-seq) data by extracting and quantifying poly(A) site usage across samples.

Key Features:

• Polyadenylation signal extraction: Extracts polyadenylation signals from RNA-seq data to detect and quantify APA events.
• RNA-Seq Poly(A) site Clustering: Groups poly(A) sites via poly(A) site clustering to define APA usage regions.
• Computational efficiency: Demonstrates at least a 7-fold faster runtime compared to alternative APA analysis methods.
• Scalability: Scales to process hundreds of samples for large-scale APA landscape analyses.
• Accuracy: Provides accurate identification and quantification of alternative polyadenylation events from RNA-seq datasets.

Scientific Applications:

• B cell activation: Applied to investigate the landscape of APA during B cell activation.
• Transcriptomic regulation and disease studies: Enables analysis of how APA influences gene expression patterns, cellular differentiation, and disease mechanisms using RNA-seq data.

Methodology:

Extracts polyadenylation signals from RNA-seq data and performs poly(A) site clustering to quantify alternative polyadenylation across samples.